The central aim of scientific inquiry has been to deduce new concepts from existing knowledge or generalized observations. The biological sciences offer numerous such challenges. The rise of deep learning has spurred major interest in using machine learning to explore uncharted molecular and functional spaces in biology and medicine, ranging from `deorphanizing' G-protein coupled receptors[1]} and translating cell-line screens to patient drug responses[2]}[3]}, to predicting novel protein structures[4]}[5]}[6]}, to identifying new cell types from single-cell omics data[7]}. Illuminating the dark space of human knowledge is a fundamental problem that one can attempt to address via deep learning—that is, to generalize a “well-trained” model to unseen data that lies out-of-the-distribution (OOD) of the training data, in order to successfully predict outcomes from conditions that the model has never before encountered. While deep learning is capable, in theory, of simulating any functional mapping, its generalization power is notoriously limited in the case of distribution shifts[8]}.
